A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images
Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing...
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| Format: | Article |
| Language: | English |
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ijoe
2023
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| Online Access: | http://eprints.uthm.edu.my/10652/1/J15859_3c4e6c8af7d98681ad3a232d0007bca9.pdf |
| _version_ | 1796870208968720384 |
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| author | Zakarya Farea Shaaf, Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil, Muhammad Mahadi Abdul Jamil Radzi Ambar, Radzi Ambar |
| author_facet | Zakarya Farea Shaaf, Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil, Muhammad Mahadi Abdul Jamil Radzi Ambar, Radzi Ambar |
| author_sort | Zakarya Farea Shaaf, Zakarya Farea Shaaf |
| collection | UTHM |
| description | Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing
such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers
selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively |
| first_indexed | 2024-03-05T22:06:08Z |
| format | Article |
| id | uthm.eprints-10652 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:06:08Z |
| publishDate | 2023 |
| publisher | ijoe |
| record_format | dspace |
| spelling | uthm.eprints-106522024-01-15T07:32:22Z http://eprints.uthm.edu.my/10652/ A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images Zakarya Farea Shaaf, Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil, Muhammad Mahadi Abdul Jamil Radzi Ambar, Radzi Ambar T Technology (General) Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively ijoe 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10652/1/J15859_3c4e6c8af7d98681ad3a232d0007bca9.pdf Zakarya Farea Shaaf, Zakarya Farea Shaaf and Muhammad Mahadi Abdul Jamil, Muhammad Mahadi Abdul Jamil and Radzi Ambar, Radzi Ambar (2023) A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images. -, 19 (2). pp. 150-162. https://doi.org/10.3991/ijoe.v19i02.36607 |
| spellingShingle | T Technology (General) Zakarya Farea Shaaf, Zakarya Farea Shaaf Muhammad Mahadi Abdul Jamil, Muhammad Mahadi Abdul Jamil Radzi Ambar, Radzi Ambar A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_full | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_fullStr | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_full_unstemmed | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_short | A Convolutional Neural Network Model to Segment Myocardial Infarction from MRI Images |
| title_sort | convolutional neural network model to segment myocardial infarction from mri images |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10652/1/J15859_3c4e6c8af7d98681ad3a232d0007bca9.pdf |
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